Executive Program in Applied Data Science & AI Strategy

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Executive Program in Applied Data Science & AI Strategy

COURSE OVERVIEW

The Applied Data Science AI Strategy course helps data professionals, analysts, students, researchers, and business leaders understand how Generative AI is transforming modern data science. Today, data teams need faster preparation, smarter modeling, automated workflows, and clearer insights. Therefore, AI skills are becoming essential for future-ready data roles.

Through this course, learners explore how GenAI supports data cleaning, preprocessing, code generation, feature engineering, AutoML, predictive modeling, synthetic data generation, visualization, and storytelling. In addition, the course connects AI concepts with practical data science and analytics use cases.

Moreover, learners understand both the opportunities and responsibilities of using AI in data-driven environments. As a result, participants become better prepared to automate workflows, generate insights faster, and use AI responsibly in analytical decision-making.

WHAT YOU'LL LEARN

By completing this Applied Data Science AI Strategy course, learners will understand how Generative AI can improve data handling, modeling, analytics, visualization, and research workflows.

Automate data handling, preprocessing, and code generation using AI tools
Use Generative AI for feature engineering, AutoML, and predictive modeling
Generate and visualize synthetic data for research and analysis
Apply responsible and explainable AI techniques in data-driven environments
Use AI tools for faster data preparation, smarter modeling, and storytelling

Overall, this course helps learners empower data, innovate faster, automate workflows, and elevate data science with Generative AI.

Empower Your Data - Innovate, Automate, and Elevate with Generative AI.

CURRICULUM

The Applied Data Science AI Strategy curriculum is divided into nine practical learning sections. Each section helps learners understand how Generative AI, analytics, automation, modeling, and responsible data science work together.

In addition, the course begins with GenAI fundamentals and gradually moves toward data automation, AI-enhanced analytics, synthetic data, explainability, visualization, and advanced data science applications. As a result, learners build both conceptual clarity and practical data-focused AI skills.

Module 1: Introduction to Generative AI and Its Evolution

Topics Covered:

This section introduces Generative AI and explains why it has become important across industries. It also helps learners understand the history, limitations, and ethical concerns connected with AI adoption.

Meaning of Generative AI
History and milestones in AI development
Traditional AI versus Generative AI
Key drivers such as big data, compute power, and neural networks
Real-world applications of GenAI
Limitations and ethical considerations

As a result, learners build a strong foundation before moving into advanced AI applications in data science and analytics.

Module 2: Fundamentals of Large Language Models (LLMs)

Topics Covered

Large Language Models are central to many modern GenAI applications. Therefore, this section explains how models such as GPT, BERT, T5, and PaLM work behind the scenes.

Transformers and attention mechanism
GPT, BERT, T5, and PaLM comparisons
Training versus inference
Prompt engineering basics
Zero-shot, few-shot, and fine-tuning approaches
Responsible use of LLMs

In addition, learners understand how LLMs can be used responsibly in data analysis, research, reporting, automation, and decision-support workflows.

Module 3: Generative AI Tools and Platforms

Topics Covered

This section introduces popular AI tools and platforms used across professional domains. Instead of focusing only on theory, learners understand how different tools support analytics, automation, coding, and business goals.

ChatGPT, Claude, Bing AI, and GitHub Copilot
Image generation tools such as DALL·E, Midjourney, and Stable Diffusion
AI tools for video, music, and voice
Open-source and proprietary GenAI tools
Tool selection based on professional goals

Moreover, this section helps learners choose suitable GenAI tools for productivity, data workflows, research, visualization, and analytical decision-making.

Module 4: Prompt Engineering and Task Design

Topics Covered

Prompt engineering helps professionals get better results from AI tools. This section focuses on writing clear prompts, designing useful tasks, and reducing weak or misleading outputs.

Anatomy of an effective prompt
Zero-shot, few-shot, and structured prompting strategies
Task types such as summarization, translation, code generation, and analysis
Tool-specific prompting examples
Reducing hallucinations and bias through prompt tuning

Consequently, learners become more confident in using AI tools for data preparation, coding support, exploratory analysis, model explanation, and business communication.

Module 5: Ethical, Legal, and Societal Implications of GenAI

Topics Covered

AI must be used with responsibility, fairness, and professional judgment. Therefore, this section explores the ethical and legal boundaries of Generative AI.

AI hallucinations and misinformation
Plagiarism and content ownership concerns
Bias and fairness in LLMs
AI and job displacement
Global perspectives on AI regulation
Responsible AI frameworks, including OECD and EU AI Act references

Furthermore, learners understand why transparency, privacy, explainability, and ethical decision-making are important in AI-powered data science.

Module 6: GenAI in Everyday Productivity & Collaboration

Topics Covered

Generative AI can improve workplace productivity when used correctly. This section explains how professionals can use AI as a practical assistant for communication, documentation, and collaboration.

Writing, summarization, and content creation with AI
AI in Excel, PowerPoint, CRM, and documents
Workflow automation with Zapier and Make
AI for meetings, brainstorming, and reporting
Chatbots and virtual assistants

As a result, learners understand how AI can support everyday work before applying it to advanced data science and analytics tasks.

Module 7: Automating Data Handling with GenAI

Topics Covered

This section connects Generative AI with data preparation, preprocessing, augmentation, and workflow automation. Learners explore how AI can reduce manual effort and improve data handling efficiency.

AI-assisted data cleaning
Data preprocessing and augmentation with AI
Automated code generation for data pipelines
Natural language queries for databases
Structured and unstructured data workflow use cases

In addition, this section shows how AI can help data teams save time, reduce repetitive work, and prepare data more effectively for analysis.

Module 8: AI-Enhanced Analytics & Modeling

Topics Covered

Modern analytics requires stronger modeling, faster experimentation, and clearer interpretation. Therefore, this section focuses on how GenAI can support feature engineering, AutoML, and predictive modeling.

GenAI for feature engineering
AI-supported model selection
AutoML with GenAI-powered assistants
Natural language explanation of model results
Predictive modeling use cases with AI support

Moreover, learners understand how AI can make modeling workflows more efficient, explainable, and accessible for different business and research needs.

Module 9: Advanced AI Research & Applications

Topics Covered

Advanced data science increasingly depends on simulation, synthetic data, multimodal analytics, and responsible AI practices. This section explores how GenAI supports innovation in research and applied analytics.

AI for simulation and hypothesis generation
Synthetic datasets for research and analysis
Generative models in text, image, and multimodal analytics
AI-driven visualization and storytelling
Responsible AI in data science, including bias, interpretability, and explainability

Finally, learners understand how to use AI responsibly for advanced data science, research innovation, visualization, and data-driven storytelling.

Live and Interactive Workshop on GenAI

This course includes a live and interactive GenAI workshop led by an expert trainer. Through this workshop, learners get real-time exposure to GenAI tools, techniques, and practical use cases across different domains.

In addition, the workshop helps learners connect course concepts with hands-on practice. Whether learners choose a generic GenAI course or a specialized track such as finance, marketing, HR, data science, cybersecurity, IoT, or operations, the workshop adds practical value to the certification journey.

See real-world GenAI applications through live demonstrations
Practise AI tools through guided hands-on activities
Interact with expert trainers and clarify important concepts
Understand how GenAI can be applied in work, studies, and projects
Build confidence to use AI tools in practical professional situations

As a result, this live workshop makes the certification more practical, industry-ready, and valuable for learners who want applied exposure to Generative AI.

WHO SHOULD ENROLL

The Applied Data Science AI Strategy course is suitable for learners and professionals who want to understand how Generative AI can improve data science, analytics, automation, modeling, and research workflows.

Moreover, the course is useful for people who want to build future-ready skills in AI-powered data preparation, AutoML, predictive modeling, synthetic data, visualization, and explainable AI.

Data scientists, analysts, and machine learning engineers
AI and data professionals exploring automation and next-generation tools
Researchers and academics working on simulation or hypothesis generation
Students and early professionals aiming to specialize in AI-powered data science
Business analysts who want to use AI for data-driven decision-making

Therefore, this course is ideal for learners who want to use AI more confidently in analytics, research, automation, and applied data science roles.

WHAT YOU'LL GET

Learners receive structured course resources, practical exposure, and certification support throughout the program. In addition, the course provides learning material that helps participants revise concepts and apply them beyond the classroom.

Industry-recognized certification upon completion
Live and interactive workshop on GenAI
Practical exposure to AI-powered data science platforms and tools
AI workflow templates and guided coding exercises
Real-world case studies across analytics and research domains
Online access to course content

Furthermore, these resources help learners understand how AI-powered data science solutions are used in analytics, automation, forecasting, research, visualization, and business strategy.

PROJECT SUBMISSION

Choose Your Project Topic

- Explore the project topics available in the LMS.
- Check the project description, learning outcomes, and required skills.
- Ask your guide/mentor if you need help selecting a topic.
- Finalize your topic and proceed.

Submit Your Project

- Follow the Project Submission Guidelines (format, font, spacing, citations).
- Your report will go through a plagiarism check, so keep it original.
- Upload your final PDF report and any supporting files to the LMS.

Project Evaluation

- Your project is evaluated by:
  - Organization Mentor
  - Internal Faculty Supervisor
- They assess you on:
  - Quality and clarity of your report
  - Depth of understanding
  - Application of concepts to real tasks
  - Problem-solving skills
  - Professional behaviour and timely submission
  - Viva/Presentation (if required)
- Credits (2–4) are awarded as per UGC NEP 2020 guidelines.

Certificates & Results

- Your Internship Certificate will be issued after:
  - Report approval
  - Evaluation completion
  - Uploading the organization certificate
- Download your Internship Completion Certificate directly from the LMS.

OUTCOME

After completing the Applied Data Science AI Strategy course, learners will be ready to automate and optimize key data science processes using Generative AI.

In addition, participants will understand how AI supports faster data preparation, smarter modeling, workflow automation, synthetic data generation, explainability, and AI-driven storytelling.

Overall, this course prepares learners to become forward-thinking data professionals who can use AI responsibly for automation, research, analytics, and future-ready decision-making.

KNOW MORE ABOUT

Explore Skilling Courses – Benefits and Offerings to understand how SkillGroom programs support practical learning, certification, career growth, and professional development.

Also, learners can browse related GenAI courses to build wider expertise in artificial intelligence, finance, marketing, HR, cybersecurity, operations, IoT, and business strategy.

Finally, choose the course that best matches your career goals and begin building practical skills for the future of data science and work.

SkILLING COURSES - BENEFITS AND OFFERINGS

Course Price: ₹4450

Who Should Enroll

Data scientists, analysts, and ML engineers seeking to integrate Generative AI into workflows
AI and data professionals exploring automation and next-gen tools
Researchers and academics working on simulation or hypothesis generation
Students and early professionals aiming to specialize in AI-powered data science